Advancements in Artificial Intelligence Applications for Forest Fire Prediction

Forests, Journal Year: 2025, Volume and Issue: 16(4), P. 704 - 704

Published: April 19, 2025

In recent years, the increasingly significant impacts of climate change and human activities on environment have led to more frequent occurrences extreme events such as forest fires. The recurrent wildfires pose severe threats ecological environments life safety. Consequently, fire prediction has become a current research hotspot, where accurate forecasting technologies are crucial for reducing economic losses, improving management efficiency, ensuring personnel safety property security. To enhance comprehensive understanding wildfire research, this paper systematically reviews studies since 2015, focusing two key aspects: datasets with related tools algorithms. We categorized literature into three categories: statistical analysis physical models, traditional machine learning methods, deep approaches. Additionally, review summarizes data types open-source used in selected literature. further outlines challenges future directions, including exploring risk multimodal learning, investigating self-supervised model interpretability developing explainable integrating physics-informed models constructing digital twin technology real-time simulation scenario analysis. This study aims provide valuable support natural resource enhanced environmental protection through application remote sensing artificial intelligence

Language: Английский

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Study on the temporal pattern and county-scale comprehensive risk assessment of wildfires in Sichuan Province

Research Square (Research Square), Journal Year: 2025, Volume and Issue: unknown

Published: April 24, 2025

Climate change and increased human activity have resulted in an increase the frequency intensity of wildfires. Effective wildfire risk assessment is essential for disaster prevention, resource protection, regional stability. Existing studies often overlook spatial heterogeneity temporal patterns wildfires, with limited county-scale quantitative assessments. To address these gaps, multidimensional framework Sichuan Province was proposed, combining characterization modeling. Temporal trends mutation wildfires from 2001 to 2023 were analyzed using Mann-Kendall test. Additionally, model constructed by hazard vulnerability Specifically, assessed Multiscale Geographically Weighted Regression (MGWR) capturing driving factors. Vulnerability through Multi-Criteria Decision Analysis (MCDA) approach identify areas high their factor importance. The results indicated a significant rise particularly during winter non-fire prevention periods. MGWR effectively captured heterogeneity, identifying highest levels southwestern Sichuan, Liangshan Prefecture Panzhihua City. High scattered, mainly across southwestern, southern, northern Sichuan. integrated revealed that its surrounding counties exhibited significantly higher than other regions, while eastern northeastern regions demonstrated lowest risk. This study provides scientific foundation targeted management, emergency response strategies Province, offering valuable insights policymakers managers.

Language: Английский

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Predicting the Duration of Forest Fires Using Machine Learning Methods

Future Internet, Journal Year: 2024, Volume and Issue: 16(11), P. 396 - 396

Published: Oct. 28, 2024

For thousands of years forest fires played the role a regulator in ecosystem. Forest contributed to ecological balance by destroying old and diseased plant material; but modern era are major problem that tests endurance not only government agencies around world, also have an effect on climate change. become more intense, destructive, deadly; these known as megafires. They can cause economic problems, especially summer months (dry season). However, humanity has developed tool predict fire events, detect them time, their duration. This is artificial intelligence, specifically, machine learning, which one part AI. Consequently, this paper briefly mentions several methods learning used predicting early detection, submitting overall review current models. Our main objective venture into new field: duration ongoing fires. contribution offers way manage fires, using accessible open data, available from Hellenic Fire Service. In particular, we imported over 72,000 data 10-year period (2014–2023) techniques. The experimental validation results than encouraging, with Random achieving lowest value for error range (8–13%), meaning it was 87–92% accurate prediction Finally, some future directions extend research presented.

Language: Английский

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An interpretable machine learning model for predicting forest fire danger based on bayesian optimization

Emergency Management Science and Technology, Journal Year: 2020, Volume and Issue: 0(0), P. 1 - 12

Published: Jan. 1, 2020

Language: Английский

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